Programmable device, hierarchical parallel machines, and methods for providing state information

ABSTRACT

Programmable devices, hierarchical parallel machines and methods for providing state information are described. In one such programmable device, programmable elements are provided. The programmable elements are configured to implement one or more finite state machines. The programmable elements are configured to receive an N-digit input and provide a M-digit output as a function of the N-digit input. The M-digit output includes state information from less than all of the programmable elements. Other programmable devices, hierarchical parallel machines and methods are also disclosed.

CLAIM OF PRIORITY

This application is a continuation of and claims priority of U.S.application Ser. No. 17/308,765 which was filed on May 5, 2021, now U.S.patent Ser. No. 11/604,687 which was issued on Mar. 14, 2023, which is acontinuation of U.S. application Ser. No. 16/197,099, which was filed onNov. 20, 2018, now U.S. Pat. No. 11,003,515, which issued on May 11,2021, which is a divisional of and claims priority to U.S. applicationSer. No. 15/353,473, which was filed on Nov. 16, 2016, now U.S. Pat. No.10,191,788 which was issued on Jan. 29, 2019, which is a divisional ofand claims priority to U.S. patent application Ser. No. 14/293,338,which was filed on Jun. 2, 2014, now U.S. Pat. No. 9,519,860 which wasissued on Dec. 13, 2016, which is a divisional of and claims priority toU.S. application Ser. No. 13/037,706, which was filed on Mar. 1, 2011,now U.S. Pat. No. 8,766,666, which was issued on Jul. 1, 2014, whichclaims priority to U.S. Provisional Application No. 61/353,551, whichwas filed on Jun. 10, 2010, which is herein incorporated by reference.

BACKGROUND

One example of a programmable device is a parallel machine. Parallelmachines include, for example, finite state machines (FSM) engines andfield programmable gate arrays (FPGAs). A FSM is a representation ofstate, transitions between states and actions. Finite state machines canbe expressed in the form of directed flow graphs. They can be used tosolve problems in, for example, engineering, pattern recognition,biology and artificial intelligence.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a hierarchical parallel machine,according to various embodiments of the invention.

FIG. 2 illustrates an example of a hierarchical parallel machineconfigured for pattern recognition, according to various embodiments ofthe invention.

FIG. 3 illustrates an example of a parallel machine, according tovarious embodiments of the invention.

FIG. 4 illustrates an example of a finite state machine diagram,according to various embodiments of the invention.

FIG. 5 illustrates another example of a finite state machine diagram,according to various embodiments of the invention.

FIG. 6 illustrates another example of a two-level hierarchy implementedwith parallel machines, according to various embodiments of theinvention.

FIG. 7 illustrates an example of a four-level hierarchy implemented withparallel machines, according to various embodiments of the invention.

FIG. 8 illustrates an example of a group of finite state machinediagrams, according to various embodiments of the invention.

FIG. 9 illustrates another example of a finite state machine diagram,wherein the final states of the diagrammatically illustrated statemachine are aggregated, according to various embodiments of theinvention.

FIG. 10 illustrates another example of a group of finite state machinediagrams, wherein the final states of each of the diagrammaticallyillustrated finite state machines are aggregated, according to variousembodiments of the invention.

FIG. 11 illustrates an example of an array of programmable elements,according to various embodiments of the invention.

FIG. 12 illustrates an example of a stack of pattern recognitionprocessors, according to various embodiments of the invention.

FIG. 13 illustrates an example of a four-level hierarchy having feedbackimplemented with parallel machines, according to various embodiments ofthe invention.

FIG. 14 illustrates another example of a four-level hierarchy havingfeedback implemented with parallel machines, according to variousembodiments of the invention.

FIG. 15 illustrates a finite state machine engine, according to variousembodiments of the invention.

FIG. 16 illustrates an example of a block of the finite state machineengine of FIG. 15 , according to various embodiments of the invention.

FIG. 17 illustrates an example of a row of the block of FIG. 16 ,according to various embodiments of the invention.

FIG. 18 illustrates an example of a group of two of the row of FIG. 10 ,according to various embodiments of the invention.

FIG. 19 illustrates an example of a method for a compiler to convertsource code into an image for programming of the finite state machine ofFIG. 8 , according to various embodiments of the invention.

FIG. 20 illustrates an example of a computer having a Von Nuemann basedarchitecture, according to various embodiments of the invention.

DETAILED DESCRIPTION

The following description and the drawings sufficiently illustratespecific embodiments to enable those skilled in the art to practicethem. Other embodiments may incorporate structural, logical, electrical,process, and other changes. Portions and features of some embodimentsmay be included in, or substituted for, those of other embodiments.

This document describes, among other things, a hierarchical parallelmachine, such as a hierarchical finite state machine (HFSM) engine, andrelated methods. One such hierarchical parallel machine 10 is shown inFIG. 1 . Each hierarchical parallel machine 10 includes two or moreparallel machines, such as two or more finite state machine (FSM)engines 12. Each finite state machine engine 12 is a device thatreceives and reacts to data (e.g., a data stream) on an input bus 16 andprovides (e.g., generates) an output as a function of the received data.

Each finite state machine engine 12 may be arbitrarily complex. In oneembodiment, such as is shown in FIG. 1 , finite state machine engines 12are cascaded, with an output (e.g., state) of at least one of the finitestate machine engines 12 provided (e.g., passed) in whole or in part toone or more of the finite state machine engines 12 downstream in thecascade. In an example, the last finite state machine engine in thecascade generates a result, which can be provided on an output bus, suchas results bus 20. In one embodiment, each of the finite state machineengines 12 can be programmed via a respective program bus 14, such asby, for example, loading (e.g., storing) a program (e.g., an image) ontoa finite state machine engine 12 using the program bus 14.

In some embodiments, it may be important to reduce the time needed toprocess data with the HPRP 10. The time to process data with the HPRP 10may be limited, at least in part, by the amount of data (e.g., stateinformation, such as a state vector) passed between finite state machineengines 12. In some such embodiments, the finite state machine engines12 are connected in a hierarchical configuration, and the interfacebetween finite state machine engines 12 can be designed to approximatereal-time operation. Among other things, this document describes suchconsiderations and suggests a general method for physical implementationof a hierarchical set of parallel machines, such as a hierarchical setof FSM engines 12.

In one example embodiment, a Hierarchical Pattern Recognition Processor(HPRP) 30 is implemented, as shown in FIG. 2 . A Pattern RecognitionProcessor is a device that receives data (e.g., a sequence of symbols)and generates an output that provides some type of notification when asequence(s) of interest is recognized (e.g., detected). In a simplecase, a single stream of input symbols is provided to HPRP 30 on aninput bus 36. The HPRP 30 is programmed to detect a specific sequence orspecific sequences of input symbols via program bus(ses) 34. Results(detected sequences) are generated, and can be provided on results bus40. Logical interfaces (Program Interface 34 and Data Input 36) of theHPRP 30 are shown in FIG. 2 .

In the embodiment shown, HPRP 30 might include two or more finite statemachine engines programmed as Pattern Recognition Processors (PRP) 32.Each PRP 32 is a device that is capable of detecting a sequence(s) ofsymbols in a respective data stream (such as a data stream on eitherinput bus 36 or input bus 42). For example, each PRP 32 can be capableof matching a respective pattern in a respective data stream. In theillustrated embodiment, the second PRP 32 receives, as its input, anoutput of the first PRP 32 (as provided on output bus 38) and generatesa result on results bus 40. In one embodiment, each of the PRPs 32 canbe programmed via a respective program bus 34. An example PRP 32 isdescribed below with reference to FIGS. 15-18 (also referred to as a“content inspection processor”). In certain examples, the HPRP 30 isimplemented using a finite state machine (FSM) engine or a fieldprogrammable gate array (FPGA), or variations thereof, or anotherimplementation of a parallel machine.

The two level (e.g., stage) hierarchy of hierarchical parallel machines10 and 30 allows two independent programs to operate based on the samedata stream. The two level hierarchy can be similar to visualrecognition in the human brain that is modeled as different regions.Under this model, the regions are effectively different patternrecognition processors, each performing a similar computational function(detecting a sequence of symbols in a data stream) but using differentprograms (e.g., signatures). By connecting multiple parallel machinestogether, deeper knowledge about the data stream may be obtained.

A first level of the hierarchy (such as one implemented by the first FSMengine 12 or first PRP 32) can perform processing directly on a raw datastream, for example. That is, the first FSM 12 or PRP 32 can generate anoutput data stream (e.g., an indication of a match or matches in the rawdata stream) as a function of the raw data stream on input bus 16 orinput bus 36, respectively. As shown in FIG. 1 , a second level (such asone implemented by the second FSM engine 12 or second PRP 32) processesan output data stream from the first level. For example, the second FSMengine 12 receives an output data stream from the first FSM engine 12(provided on output bus 18) and the second FSM engine 12 processes anoutput data stream of the first FSM engine 12. Accordingly, the secondFSM engine 12 does not receive the raw data stream as an input, butrather receives an output data stream generated by the first FSM engine12. The second FSM engine 12 can be programmed with a first image todetect a sequence(s) in the output data stream generated by the firstFSM engine 12. The second FSM engine 12 may be coupled to a separateprogramming interface (e.g., by a program bus 14) for receiving a secondimage.

In an example, HPRP 30 can be programmed to implement a patternrecognition function. For example, a FSM implemented by HPRP 30 can beconfigured to recognize one or more data sequences (e.g., signatures) ina data stream input to the HPRP 30. When a sequence of interest in thedata stream is recognized (e.g., matched) by the first PRP 32, an outputdata stream indicating that recognition can be provided on output bus38. In an example, the pattern recognition can be to recognize a stringof symbols (e.g., ASCII characters) to, for example, identify malware orother information in network data.

This output data stream (e.g., an output word, detection state, etc.)can be fed from the output bus 38 of a first PRP 32 to an input bus 42of another PRP 32 as shown in FIG. 2 . This connection of two PRPs 32 inseries provides a means to provide information regarding past events ina compressed word from a first PRP 32 to a second PRP 32. This provisionof information can effectively be a summary of complex events (e.g.,data stream sequences) that were recognized by the first PRP 32.

As noted above, in some embodiments it may be important to reduce thetime needed to pass output between levels of PRPs. In some suchembodiments, the interface between PRPs 32 can be designed to supportreal-time operation of each level of HPRP 30. This document describessuch considerations and suggests a general method for physicalimplementation of a HPRP 30, for example.

This document describes, among other things, methods and apparatus forprocessing data using a hierarchical structure. The hierarchicalstructure can comprise a plurality of levels (e.g., layers), where eachlevel processes (e.g., performs an analysis on) data and provides anoutput (e.g., based on the analysis). The output from lower levels inthe hierarchical structure can be provided as inputs to higher levels.In this manner, lower levels can perform more basic/fundamentalanalysis, while a higher level can perform more complex analysis usingthe outputs from one or more lower levels. In an example, thehierarchical structure performs pattern recognition.

In an example, the hierarchical structure is implemented with aplurality of finite state machine engines coupled together in acascading manner. For example, a first and second finite state machineengine can be coupled in series such that the second finite statemachine engine receives, as an input, an output from the first finitestate machine engine. Any number of finite state machine engines can becoupled together in this hierarchical structure.

In addition to processing data using a hierarchical structure, thisdocument also describes methods and apparatuses for using output fromone finite state machine engine to modify the processing performed byanother finite state machine engine. Using the finite state machineengine example described above, the second finite state machine engineimplementing a higher level of processing can provide feedbackinformation to the first finite state machine engine implementing alower level of processing. The feedback information can be used by thefirst finite state machine engine to modify (e.g., update) theprocessing in a manner similar to learning in a biological brain.

FIG. 3 illustrates an example parallel machine 100 that can be used toimplement a finite state machine engine or a pattern recognitionprocessor. The parallel machine 100 can receive input data and providean output based on the input data. The parallel machine 100 can includea data input port 110 for receiving input data and an output port 114for providing an output to another device. The data input port 110provides an interface for data to be input to the parallel machine 100.

The parallel machine 100 includes a plurality of programmable elementsincluding general purpose elements 102 and special purpose elements 112.A general purpose element 102 can include one or more inputs 104 and oneor more outputs 106. A general purpose element 102 can be programmedinto one of a plurality of states. The state of the general purposeelement 102 determines what output(s) the general purpose elements 102will provide based on a given input(s). That is, the state of thegeneral purpose element 102 determines how the programmable element willreact (e.g., respond) to a given input. Data input to the data inputport 110 can be provided to the plurality of general purpose elements102 to cause the general purpose elements 102 to take action thereon.Examples of a general purpose element 102 can include, for example, astate machine element (SME), as discussed in detail below, a counter,and/or a configurable logic block, among other programmable elements. Inan example, a SME can be programmed (e.g., set) to provide a certainoutput (e.g., a high or “1” signal) when a given input is received atthe data input port 110. When an input other than the given input isreceived at the data input port 110, the SME can provide a differentoutput (e.g., a low or “0” signal). In an example, a configurable logicblock can be set to perform a Boolean logic function (e.g., AND, OR,NOR, ext.) based on input received at the data input port 110. Anexample of a counter is discussed later herein. A special purposeelement 112 can include memory (e.g., RAM), logic gates, counters,look-up tables, field programmable gate arrays (FPGAs), and otherhardware elements. A special purpose element 112 can interact with thegeneral purpose elements 102 and performing special purpose functions.

The parallel machine 100 can also include a programming interface 111for loading a program (e.g., an image) onto the parallel machine 100.The image can program (e.g., set) the state of the general purposeelements 102. That is, the image can configure the general purposeelements 102 to react in a certain way to a given input. For example, ageneral purpose element 102 can be set to output a high signal when thecharacter ‘a’ is received at the data input port 110. In some examples,the parallel machine 100 can use a clock signal for controlling thetiming of operation of the general purpose elements 102. In someembodiments, the data received at the data input port 110 can include afixed set of data received over time or all at once, or a stream of datareceived over time. The data may be received from, or generated by, anysource, such as databases, sensors, networks, etc, coupled to theparallel machine 100.

The parallel machine 100 also includes a plurality of programmableswitches 108 for selectively coupling together different elements (e.g.,general purpose element 102, data input port 110, output port 114,programming interface 111, and special purpose elements 112) of theparallel machine 100. Accordingly, the parallel machine 100 comprises aprogrammable matrix formed among the elements. In an example, aprogrammable switch 108 can selectively couple two or more elements toone another such that an input 104 of a general purpose element 102, thedata input port 110, a programming interface 111, or special purposeelement 112 can be coupled through one or more programmable switches 108to an output 106 of a general purpose element 102, the output port 114,a programming interface 111, or special purpose element 112. Thus, therouting of signals between the elements can be controlled by setting theprogrammable switches 108. Although FIG. 3 illustrates a certain numberof conductors (e.g., wires) between a given element and a programmableswitch 108, it should be understood that in other examples, a differentnumber of conductors can be used. Also, although FIG. 3 illustrates eachgeneral purpose element 102 individually coupled to a programmableswitch 108, in other examples, multiple general purpose elements 102 canbe coupled as a group (e.g., a block 802, as illustrated in FIG. 15 ) toa programmable switch 108. In an example, the data input port 110, thedata output port 114, and/or the programming interface 111 can beimplemented as registers such that writing to the registers providesdata to or from the respective elements.

In an example, a single parallel machine 100 is implemented on aphysical device, however, in other examples two or more parallelmachines 100 can be implemented on a single physical device (e.g.,physical chip). In an example, each of multiple parallel machines 100can include a distinct data input port 110, a distinct output port 114,a distinct programming interface 111, and a distinct set of generalpurpose elements 102. Moreover, each set of general purpose elements 102can react (e.g., output a high or low signal) to data at theircorresponding input data port 110. For example, a first set of generalpurpose elements 102 corresponding to a first parallel machine 100 canreact to the data at a first data input port 110 corresponding to thefirst parallel machine 100. A second set of general purpose elements 102corresponding to a second parallel machine 100 can react to a seconddata input port 110 corresponding to the second parallel machine 100.Accordingly, each parallel machine 100 includes a set of general purposeelements 102, wherein different sets of general purpose elements 102 canreact to different input data. Similarly, each parallel machine 100, andeach corresponding set of general purpose elements 102 can provide adistinct output. In some examples, an output port 114 from firstparallel machine 100 can be coupled to an input port 110 of a secondparallel machine 100, such that input data for the second parallelmachine 100 can include the output data from the first parallel machine100.

In an example, an image for loading onto the parallel machine 100comprises a plurality of bits of information for setting the state ofthe general purpose elements 102, programming the programmable switches108, and configuring the special purpose elements 112 within theparallel machine 100. In an example, the image can be loaded onto theparallel machine 100 to program the parallel machine 100 to provide adesired output based on certain inputs. The output port 114 can provideoutputs from the parallel machine 100 based on the reaction of thegeneral purpose elements 102 to data received at the input port 110. Anoutput from the output port 114 can include a single bit indicating amatch of a given pattern, a word comprising a plurality of bitsindicating matches and non-matches to a plurality of patterns, and anoutput vector corresponding to the state of all or certain generalpurpose elements 102 and special purpose elements 112.

Example uses for the parallel machine 100 include, pattern-recognition(e.g., speech recognition, image recognition, etc.) signal processing,imaging, computer vision, cryptography, and others. In certain examples,the parallel machine 100 can comprise a finite state machine (FSM)engine, a field programmable gate array (FPGA), and variations thereof.Moreover, the parallel machine 100 may be a component in a larger devicesuch as a computer, pager, cellular phone, personal organizer, portableaudio player, network device (e.g., router, firewall, switch, or anycombination thereof), control circuit, camera, etc.

A parallel machine (e.g., a FSM engine, PRP or the like) can implement astate machine(s). A state machine can be represented as a directedgraph. FIG. 4 shows a simple state machine diagram 150 that representsthe sequence of characters found in the word ‘DOG’. State 152 is theinput state for state machine diagram 150. State 154 is an intermediatestate. In FIG. 4 , the final state 156 (sometimes also called terminalstate) is identified by the dotted line border around the ‘G’ state. Inthe general case, when the final state is reached, a match condition isindicated through some mechanism. This match condition may berepresented by an explicit signal from a parallel machine 100 (e.g., FSMengine 12, PRP 32), or it may be encoded as a binary word and stored ina memory register.

There is no theoretical limit to the size of a state machine. In thegeneral case, a PRP or FSM engine might implement a separate statemachine for each specific sequence of symbols that can be detected bythe PRP or FSM engine. One can, if desired, perform optimizations on thestate machines in order to eliminate redundancies (common paths), suchas to combine state machines into a larger implementation or to minimizethe size of a particular state machine implementation. Suchoptimizations can reduce the aggregate size of the state machineimplementation(s), and thus, for example, a state machine engineimplementing the state machine(s). Once this optimization is complete, asingle large state machine may be implemented.

FIG. 5 shows a larger state machine diagram 200. In the general case, astate machine implementation may have complex connections both forwardand backwards. In the example shown in FIG. 5 , one input state 202feeds two intermediate states 204. In such state machines, there may bemany final states 208 and many other intermediate states 204.

Each state in a state machine has an instantaneous status that indicateswhether the state is active. Only active states can react to an inputsymbol. In one embodiment, when an input symbol is received on input bus36, each active state in the state machine will analyze that symbol todetermine if an activation signal should be generated. This activationsignal will be used to activate the next state in the sequence. Forexample, a first state 204 that specifies the character ‘b’ willactivate a second state 204, connected to the first state 204 by atransition 206, on the input character ‘b’ when the first node 204 isactive and the character ‘b’ is received as input data.

In the diagram 200, the input state 202 can be initially activated andcan activate a downstream state 204 when the input data matches atransition 206 from the input node 202. States 204, 208 throughout thediagram 200 can be activated in this manner as the input data isreceived. An activated final state 208 corresponds to a match of asequence of interest by the input data. Accordingly, activation of afinal state 208 indicates that a sequence of interest has been receivedat the input data. In the context of a finite state machine engine 100implementing a pattern recognition function, activating a final state208 can indicate that a specific pattern of interest has been detectedon the input data.

In an example, each intermediate state 204 and final state 208 cancorrespond to a general purpose element 102 in the finite state machineengine 100. Each transition 206 can correspond to connections betweenthe general purpose elements 102. Thus, an intermediate state 204 thattransitions to (e.g., has a transition 206 connecting to) anotherintermediate state 204 or a final state 208 corresponds to a generalpurpose element 102 that can be coupled to another general purposeelement 102. In some special cases, the start state 202 may notnecessarily have a corresponding general purpose element 102.

When a finite state machine engine 100 is programmed to implement a FSM,each of the general purpose elements 102 can be in either an active orinactive state. An inactive general purpose element 102 does not reactto the data stream at the input interface 110. An active general purposeelement 102 can react to the data stream at the input interface 110, andcan activate a downstream general purpose element 102 when the inputdata stream matches the setting of the general purpose element 102. Whena general purpose element 102 corresponds to a final state 208, thegeneral purpose element 102 can be coupled to the output port 114 toprovide an indication of a match to an external device, which in somecases could be another finite state machine engine 100.

An image loaded onto the finite state machine engine 100 via theprogramming interface 111 can configure the general purpose elements 102and the connections between the general purpose elements 102 such that adesired FSM is implemented through the activation of downstream statesbased on responses to the data stream at the input interface 110. In anexample, a general purpose element 102 remains active for a single datacycle (e.g., a single character, a set of characters, a single clockcycle) and then switches to inactive unless re-activated by an upstreamgeneral purpose element 102.

A final state 208 can be considered to store a compressed history ofpast events. For example, the one or more sequences of input datarequired to activate a final state 208 can be represented by theactivation of that final state 208. In an example, the output providedby a final state 208 is binary, that is, the output indicates whether acorresponding sequence of interest has been matched or not. The ratio offinal states 208 to intermediate states 204 in a FSM may be quite small.In other words, although there may be a high complexity in the FSM, theoutput of the FSM may be small by comparison.

Whether a FSM engine implements a single combined (optimized) statemachine, or implements many independent state machines, the concept of astate vector exists. A state vector is a one dimensional vector that hasa one-to-one correspondence between the individual states in theimplemented state machine(s) and the individual digit (e.g., bit)positions within the vector. That is to say that each state in the statemachine(s) is related to a digit in the state vector. In the case ofFIG. 4 , the state vector is 3 bits wide (with one bit indicating thestate of each of states 152, 154 and 156). In the case of FIG. 5 , thestate vector is 74 bits wide. State machines can be arbitrarily complexand therefore, in theory, there is no limitation placed on the overallsize of the machine. Consequently, the state vector can be infinitelylong.

In order to implement practical state machines in parallel machines,however, some finite limitation usually is placed on the size of thestate machines. This limit is not strictly defined and can be determinedbased on the characteristics of the parallel machine used to implementthe state machines.

Another HFSM 400 is shown in FIG. 6 . In the HFSM 400 shown in FIG. 6 ,three finite state machine engines 402 provide information regardingtheir respective states to one finite state machine engine 404 using allor a portion of all their respective state vector digits. In the exampleshown, each state machine engine (402, 404) is programmed via itsrespective programming interface (PROG). In other embodiments, data fromeach of FSM engines 402 is used to program FSM engine 404. In some suchembodiments, FSM engine 404 is designed to adapt to the stateinformation received from FSM engines 402.

A more complex HFSM 500 is shown in FIG. 7 . In the HFSM 500 of FIG. 7 ,multiple FSM engines 502, 504, 506, 508 are connected together, with FSMengine 502 providing (e.g., feeding) state information through bus 510to FSM engine 504, FSM engine 504 feeding state information through bus512 to FSM engine 506, and FSM engine 506 feeding state informationthrough bus 514 to FSM engine 508. This connecting of multiple FSMlevels 502-508 allows each level of the hierarchy to implement differentstate machines. In some example HFSMs, each level of the hierarchy issensitive to different types of patterns. In these example HFSMs, as inHFSM 500, this separation of hierarchical levels allows the HFSM toimplement low level recognition that is passed through various levels ofthe hierarchy to achieve higher level recognition. In one example, aresult is provided on a results bus 516 of HFSM 500, such as, e.g.,identification of a particular pattern (e.g., a phrase). In otherexamples, the result is a combination of state bits from one or more ofFSM engines 502, 504, 506, and 508.

As shown in FIG. 7 , one approach for connecting individual FSM enginesin a hierarchical manner is to connect the output of one FSM engine tothe input of the next higher level FSM engine in the hierarchy. Itshould be understood that a HFSM 500 could be implemented in which stateinformation from one level is provided (e.g., fed forward or back) toany other level in the hierarchy. For instance, state information fromFSM engine 502 might be sent to FSM engine 506, while state informationfrom FSM engine 508 might be fed back to FSM engine 502. In generalterms, state information from one or more FSM engines can be provided toone or more (e.g., all) of the other FSM engines in whateverconfiguration is deemed necessary.

The example shown in FIG. 7 corresponds to a visual identification ofwritten language. As the processing progresses to higher levels of thehierarchy, the accumulated knowledge of the data stream growscorrespondingly. In the embodiment shown, the FSM engine at each level(FSM engines 502, 504, 506 and 508) are cascaded to accomplishhierarchical recognition capability. Each successive level of thehierarchy can implement new rules (pattern signatures) that are appliedto the output of the previous level. In this way, highly detailedobjects can be identified based on the detection of very basic primitiveinformation.

For example, the raw data input stream to level one (e.g., the first FSMengine 502) can comprise pixel information (e.g., whether a given bit isblack/white or on/off) for an image. FSM engine 502 can be programmed torecognize (e.g., identify) primitive patterns formed by the bits. In oneexample, FSM engine 502 is configured to identify when a group ofadjacent bits form vertical lines, horizontal lines, arcs, etc. Each ofthese patterns can be identified by a separate output bit (or signal)from FSM engine 502. For example, when FSM engine 502 recognizes avertical line of at least 3 bits, a high signal (e.g., logical one) canbe sent on a first bit of an output word to FSM engine 504. When FSMengine 502 identifies a horizontal line of at least 3 bits, a highsignal can be sent on a second bit of an output word to FSM engine 504.

FSM engine 504 meanwhile can be programmed to identify patterns formedby the output 510 from the FSM engine 502. For example, FSM engine 504can be programmed to identify patterns formed by combinations of theprimitive patterns (lines, arcs, etc.) identified by FSM engine 502. FSMengine 504 can be programmed to identify when a horizontal line and avertical line cross forming the letter “t”, for instance. As mentionedabove, the HFSM 500 implemented using FSM engine 504 reacts to theoutput from FSM engine 502. Thus, the combinations of the primitivepatterns are identified by identifying patterns in the output bits fromFSM engine 502.

The output 512 from FSM engine 504 is then input into FSM engine 506,which can identify words from combinations of the letters identified byFSM engine 504. The fourth level (FSM engine 508) can then identifyphrases formed by the words identified by FSM engine 506. Accordingly,higher levels can be programmed to identify patterns in the lower leveloutputs. Additionally, lower levels can be programmed to identifycomponents that make up the patterns identified in the higher level(feedback to lower levels).

The visual identification of letters is used as an example. Thehierarchical method and system described herein can, however, be appliedto other data. For example, hierarchical processing on datacorresponding to sounds can identify syllables from combinations ofphonemes at level one and words from combinations of syllables at leveltwo. In other examples, the hierarchical processing can be applied tomachine data that builds upon itself in a hierarchal manner.

When implementing a HPRP or HFSM, such as HFSM 500, one problem that canbe encountered is the asymmetric relationship between input data andoutput data. This asymmetry is exacerbated when the state machine(s)being implemented becomes sufficiently large. For each input symbol thatis processed, the state vector of a FSM engine can change responsive tothe input symbol. In one embodiment, each FSM includes, for example, upto 2 to the 16th power (64K) states. If each state has a correspondingdigit in the state vector, the state vector would be 64K bits long. Itcan be difficult to pass vectors of that length between FSM engines.Ways to reduce this asymmetry between the size of the input data and thesize of the output data will be described next.

In one embodiment, data is sent between FSM engines on a bus. An N-bitwide bus can pass a 64 Kb state vector in 64 Kb/N cycles. In otherembodiments, the state vector is compressed such that only digits in thevector that change in response to an input symbol propagate to the otherFSM engines. For instance, each bus cycle might include the location ofa digit in the state vector that changed in the previous symbol cycle.In this case, the output can be referred to as a difference vector.

In yet another embodiment, each FSM engine is designed to send only asubset of digits from the state vector to the other FSM engines. In onesuch embodiment, each FSM engine is programmed such that the only stateinformation passed to the other FSM engines is that of final states. Inthe general case, the number of final states is smaller than the totalnumber of states. The ratio of final states to total states in a PRP 32,for example, is dependent upon the state machines that are implementedin the PRP. Ratios can be high (1:5) or quite low (1:10,000), forexample.

In a practical example where the ratio of final states to total statesis 1:10 and the state vector is 64 Kb, this implies that an outputvector would be 64 Kb/10 or 6,554 bits. In this example, every inputcycle of the PRP (8 bit symbols) would generate a corresponding outputvector of 6,554 bits.

For the examples going forward, we will use an example where the ASCIIcharacter set is the input (symbol) language. In this case, each inputsymbol is represented by an 8-bit binary word. In alternativeembodiments, other symbols can be used, such as where each input symbolis an N-bit binary word.

For instance, in one embodiment, the output vector sent to the secondlevel PRP 32 in a HPRP 30, such as that depicted in FIG. 2 , mightrepresent only the final states of the first level PRP 32. In anotherexample, in a HFSM 500, such as that depicted in FIG. 7 , FSM engine 504can generate an output vector representing its final states and send theoutput vector to FSM engine 506 on output bus 514. In one such example,output bus 514 is an eight bit wide bus. Using the 8:6,554 ratio citedfrom the previous example, the number of cycles needed to provide (e.g.,transfer) the 6,554 bits of the output vector would be 6,554/8, or 820,cycles. That is, each successive level of the hierarchy would require820 input cycles to process the output word from the previous level.This effect ripples through the hierarchy linearly, so that eachsuccessive state will require 820 input cycles to resolve its inputword. In this case, with the given ratios, a six level hierarchy ofPRP's would require 4,100 (5×820) input cycles to allow the input symbolto ripple through until a result is produced at the highest level. Thesenumbers only serve as an example. If the final state to total stateratio is increased, the ripple time will increase. Likewise if thenumber of levels of the hierarchy is increased, the ripple time willincrease linearly with each successive level.

Based on the examples used above, a delay of several orders of magnitude(relative to the input cycle) is possible for a HFSM or HPRP to producea result. Delays of this type may be unacceptable in real-timeapplications. As noted above, one can reduce the number of cycles neededto transfer the state information by, for instance, increasing the sizeof the bus. One can also decrease the bus cycle time to reduce the timeneeded to transfer the state information. Further, as noted above, onecan send a difference vector which only identifies the digits of thestate vector that changed as a result of the last symbol. Other losslesscompression mechanisms can be used as well.

Other methods to reduce this delay are discussed next, such asimplementing a 1:1 relationship between input and output.

One way to get a 1:1 relationship between input and output in a HFSM,such as HFSM 500, is to make the input bus 518 and the output bus 510equal in size (width). The width of output bus 510 can be determined bythe HFSM 500 itself. For example, the size of output bus 510 could bedetermined by the number of final states in the state machine engine502. Generally speaking, a FSM engine is programmed to recognize manydifferent patterns at once. Each of these patterns may be implemented asan individual state machine. In this way, a FSM engine may implement aset of state machines, all running in parallel.

FIG. 8 diagrammatically shows an example of a group 530 of statemachines 532 all running in parallel within a single FSM engine. In FIG.8 , eight state machines 532 are shown. In practice a FSM engine mayimplement thousands of individual state machines.

Step 1: Aggregation of State Machine Outputs (Final States)

As shown in FIG. 9 , each individual state machine 532 may have one ormore final states 208. Although there may be several final states 208,any one of the final states of a particular state machine 532 has thesame, or a related meaning. Another way of saying this is that a statemachine 532 is considered to have a match if ANY of the final states 208of the state machine is reached. Effectively, this means that the finalstates 208 of a single state machine 532 may be aggregated (e.g., OR'dtogether), such as by using an OR gate 540 to couple together theoutputs of the programmable elements corresponding to the finals states,to provide a single output 542 as shown in FIG. 9 . In the example shownin FIG. 9 , state machine 532 ORs together three final states 208. Ascan be understood, other logic could be used to aggregate the finalstates 208.

In one example, once the final states 208 of each state machine 532 havebeen aggregated (e.g., OR'd), the results are grouped (e.g., collected)in logical groups of N state machines 532, where N is equal to thenumber of digits in the respective input symbol language. In an examplewhere the input symbol language of a first level comprises 8-bit inputwords, 8 individual state machine outputs 542 could be aggregated toprovide one of the input symbols 546 provided to the next level of thehierarchy. In FIG. 7 , for example, only the first level of thehierarchy receives input symbols that correlate to a standard language(ASCII or other). In that example then, FSM engine 502 might generate aneight bit output vector that is provided to FSM engine 504 on an outputbus 510. Subsequent levels of the hierarchy receive input symbols thathave meaning that is determined by the previous level.

Once state machines have been grouped, such as in sets of 8, the firstlevel of normalizing the input and output vectors has been completed.Using the numbers from the example used in this disclosure, 820 finalstates could be represented in 103 8-bit words. Each of these 8-bitwords encodes the status of the final states for 8 individual statemachines 532. Keep in mind that the total number of final states encodedin this 8 bit output vector may be much greater than 8. This is becausethe OR'ing function performed on final states 208 in the same statemachine 532 may OR more than eight states together.

In one embodiment, each FSM engine includes an N bit-wide input port andan N bit-wide output port. In one embodiment, state information fromeach level (e.g., in the form of an output vector, such as all or aportion of a state vector, or a difference vector) is distributedthrough an N bit bus to the next FSM engine. For instance, FSM engine502 distributes state information to FSM engine 504 using the N-bitoutput bus 510. In one embodiment, the same N-bit output vector isprovided (e.g., distributed) to each state machine in FSM engine 504. Inanother embodiment, programmable elements in FSM engine 504 are groupedinto groups (e.g., blocks) and the output port of FSM engine 502 writesa sequence of N-bit words to FSM engine 504 and the sequence of words isdistributed to the blocks of state machine elements in FSM engine 504 ina pre-defined manner (such as sequentially). Such an approach allows thedistribution of additional state information to FSM engine 504, butrequires additional bus cycles to transfer complete state information.

In one embodiment, state information for one group of programmableelements is sent to a group of programmable elements in another FSMengine by sending address information including an indication of the FSMengine and the address of the group within the FSM engine. Thatinformation could be distributed, for example, on bus 510 in FIG. 7 .

Step 2: Expansion of the Number of Input Busses on the PRP

One implementation of a FSM 12 or a PRP 32 has a single stream inputthat broadcasts an input symbol to all state machines 532 implemented inthe PRP. The definition of FSM 12 and PRP 32 may, however, be extendedto implement more than one stream input (16 and 36 respectively). In theexample previously cited, the total number of independent stream inputswould equal 103. To completely implement, for example, a HPRP would thenrequire 103 8-bit inputs or an input bus of 820 bits for each PRP 32.

In one embodiment, FSM 12 and PRP 32 are implemented on an array 560 ofprogrammable elements, such as state machine elements (SMEs). In oneexample, each SME is in a homogenous two-dimensional array 560. Thisarray 560 may be subdivided into individual regions with each regionhaving it's own dedicated stream input (16 and 36, respectively). FIG.11 shows such a two dimensional array 560 of SME elements. FIG. 11 issubdivided into an array 560 of SME groups 562, wherein each group 562of SMEs may correspond to a block 802 in finite state machine engine800, for example. The entire array 560 might include, for example, 16×16SME groups (256 groups, total), with each group 562 including 128 groupsof two SMEs (GOTs) (e.g., where each group 562 includes 16 rows of GOTs,such as rows 806 illustrated in FIG. 16 ).

In some embodiments, each row of GOTs contains eight GOTs, an additionalprogrammable element(s) (e.g., Boolean logic or a counter) and canprovide two outputs to the output bus 18, 38. If all available outputsare used across the FSM 12 and PRP 32, such an array may have, forexample, up to 8192 bits to drive to the next level PRP 32.

When a HFSM 500 is constructed such as is shown in FIG. 7 , the twodimensional arrays of SME groups 562 in two different semiconductordevices, such as FSM engines 502 and 504, may be connected together.There are various means of connecting two semiconductor devicestogether. When the I/O count gets sufficiently high, die to dieinterconnect may be utilized, for example. In one exampleimplementation, as is shown in FIG. 12 , each one of the 256 SME groups562 in a HPRP 570 could have an 8-bit interface (e.g., input bus 36, 42)on the bottom of a die and an 8 bit interface (e.g., output bus 38, 40)on the top of the die. When these interfaces are put in predefinedlocations, one level of PRP (PRP1) 582 can be stacked directly on top ofa lower level PRP (PRP0) 580, with the input and output interfacesnaturally aligned and connected together using interconnects (e.g.,through-silicon vias), such as interconnect 574.

This alignment effectively creates the concept of a SME column (definedby input path 572, output path 578 and interconnects 574 and 576) witheach level of the column representing a group of SMEs contained on asame die. Continuing to use the example numbers previously discussed, oneach PRP level (580, 582 and 584), an SME group 562 can be driven by upto 8 state machines implemented on the previous level. The 8 statemachines from the previous level may be arbitrarily complex up to thelimit imposed by the SME group 562. FIG. 12 shows an example of athree-level HPRP (edge view) with one of the SME columns highlighted. Ineach level a grouping of SMEs provides state information from that level(e.g., an encoded 8 bit word) to the next higher level.

Overall, when configured in this way, a HPRP can provide substantiallyinstantaneous results with the delay of only one input clock cycle foreach level of the PRP hierarchy. The problem posed by the asymmetry ofthe input and output words is resolved and the entire hierarchy mayoperate in sync with the stream input 572.

In certain embodiments, state information from one FSM engine 12 is sentto more than one other FSM engine 12. Such an embodiment is shown inFIG. 6 . Describing such an embodiment with reference to the HPRP 570illustrated in FIG. 12 , for instance, state information from PRP 580could be sent to PRP 584. In one such embodiment, interconnect 576 and574 form a bus that transmits state information to any of the blocks inthe column. For example, interconnects 574 and 576 may comprise one ormore through via interconnects, and can be provided in each column forpassing state information to nonadjacent PRPs. In one such embodiment,each PRP in the stack is connected to and receives information from thethrough vias. In another embodiment, inputs of PRP are selectivelyconnected to through vias during the manufacturing process as needed.

In other embodiments, state information from one group of SMEs isdistributed to adjacent blocks in the same PRP 32, and through thoseblocks to other PRPs 32 (e.g., in the same block column).

In one embodiment, state information from one or more PRPs 32, orinformation derived from that state information, is used to reprogramother PRPs 32 in the hierarchy. FIG. 13 illustrates an example of a fourlevel hierarchy that uses feedback to reprogram portions of thehierarchy. In general a given PRP 32 (e.g., the first finite statemachine engine 602) can be reprogrammed based on an output from a higheror lower level finite state machine engine or based on its own output.Thus, the first finite state machine engine 602 can change to adapt tochanging conditions during run-time. In an example, the feedback can beused for the lower levels to learn (be reprogrammed) based on the higherlevels. The feedback, using the finite state machine engine 602 as anexample, can be received at the programming interface 602B and can be inthe form of a new or updated program for the finite state machine engine602. In an example, the updated program can reprogram some or all offinite state machine engine 602.

The four-level hierarchy 600 in FIG. 13 is implemented with four finitestate machine engines 602, 604, 606, 608 which each have a an input port602A, 604A, 606A, 608A, a programming interface 602B, 604B, 606C, 608B,and an output port 602C, 604C, 606C, 608C. The first finite statemachine engine 602 implements the first level of the hierarchy 600 andprovides an output to the second finite state machine engine 604 whichimplements the second level of the hierarchy 600. The third and fourthfinite state machine engines 606, 608 likewise implement the third andfourth levels of the hierarchy 600. In an example, the output from thefourth finite state machine engine 608 is sent to an external device asan output of the hierarchy 600 based on analysis of the hierarchy 600 onthe input data received by the first finite state machine engine 602.Accordingly, the output from the fourth finite state machine engine 608corresponds to the collective output for the hierarchy 600. In otherexamples, the output from other ones of the finite state machine engines602, 604, or 606 can correspond to the collective output for thehierarchy 600.

The outputs from the second, third, and fourth finite state machineengines 604, 606, 608 can each be fed back to the programming interface602B, 604B, 606B of the finite state machine engine 602, 604, 606 at thelevel below. For example, the output from the fourth finite statemachine engine 608 is fed back into the programming interface 606B ofthe third finite state machine engine 606. The third finite statemachine engine 606, therefore, can be reprogrammed based on the outputfrom the fourth finite state machine engine 608. Accordingly, the thirdfinite state machine engine 608 can modify its program during runtime.The first and second finite state machine engines 602, 604 can besimilarly reprogrammed during runtime based on the outputs from thesecond and third finite state machine engines 604, 606 respectively.

In an example, the feedback from an output from a finite state machineengine 604, 606, 608 is processed (e.g., analyzed and compiled) to forma program for reprogramming a finite state machine engine 602, 604, 606.For example, the output from the finite state machine engine 608 can beanalyzed and compiled by a processing device 614 before being sent tothe programming interface 606B. The processing device 614 can generatethe updated program for the finite state machine engine 606 based on theoutput from the finite state machine engine 608. The processing device614 can analyze the output and compile the updated program for the thirdfinite state machine engine 606. The updated program can then be loadedonto the third finite state machine engine 606 through the programminginterface 606B to reprogram the third finite state machine engine 606.In an example, the updated program may contain only a partial changefrom the current program. Thus, in an example, an updated programreplaces only a portion of a current program on a finite state machineengine 602, 604, 606, 608. In another example, an updated programreplaces all or a large portion of a current program. Likewise, theprocessing devices 610, 612 can analyze and compile feedback in asimilar manner based on the outputs from the second and third finitestate machine engines 604, 606. A processing device 610, 612, 614 can beimplemented with one or more additional finite state machines engines,or can be implemented with a different type of machine (e.g., a computerhaving a von Nuemann architecture).

In some examples, the processing devices 610, 612, 614 analyze theoutput from a higher level prior to compiling the new program. In anexample, the processing devices 610, 612, 614 analyze the output todetermine how to update the lower level program and then compile the newor updated lower level program based on the analysis. Although in thehierarchy 600, the feedback at a given finite state machine engine isreceived from the level directly above the given finite state machineengine, feedback can be from any level finite state machine engine toanother finite state machine engine at a higher, lower, or the samelevel. For example, feedback can be received at a programming input of afinite state machine engine from the output of that same finite statemachine engine, or from the output of another finite state machineengine at the same, higher, or lower levels. Additionally, a finitestate machine engine can receive feedback from multiple different finitestate machine engines. The reprogramming of finite state machine enginesbased on feedback may be disconnected in time from the identification ofpatterns in the input data (e.g., not real time with the processing ofthe raw data).

A purpose of sending information back down the hierarchy to affectreprogramming of the lower levels can be so that the lower levels maybecome more efficient at discerning patterns of interest. In someexamples, the process of sending information to higher levels is avoidedwhen possible, recognizing that it takes time to transfer information tohigher levels of the hierarchy. In some examples, the higher levels canbe essentially used to resolve the identification of patterns that arenew to the system. This can be similar to the process used that takesplace in the neocortex of a biological brain. In an example, if apattern can be fully resolved at the lower levels, it should be. Thefeedback mechanism is one method to transfer “learning” to the lowerlevels of the hierarchy. This process of pushing information back downthe hierarchy will help preserve the upper levels of the hierarchy forprocessing new or unfamiliar patterns. Furthermore, the entirerecognition process can speed up by reducing the amount of data transferthrough various levels of the hierarchy.

The feedback can make the lower levels of the hierarchy more acutelysensitive to the data stream at the input. A consequence of this “pushdown” of information is that decisions can be made at the lower levelsof the hierarchy and can be done so quickly. Accordingly, in an example,the output from lower level finite state machine engines (e.g., thefirst finite state machine engine 602) can correspond to the collectiveoutput from the hierarchy 600 to another device along with the outputfrom the fourth finite state machine engine 608. The external devicecan, for example, monitor the output from each of these finite statemachine engines 602, 608 to determine when patterns have been identifiedby the hierarchy 600.

In an example, the feedback information can include identifyinginformation corresponding to the data stream analyzed. For example, theidentifying information can include an identifying characteristic of thedata, format of the data, a protocol of the data, and/or any other typeof identifying information. The identifying information may becollected, analyzed, and used to adapt the analysis method for the inputdata by, for example the processing device 610. A finite state machineengine may then be programmed with the adapted analysis method. Theidentifying information can include, for example, a language of theinput data. The finite state machine engine can be initially programmedto determine a language of the input data and may be adapted (e.g.,reprogrammed) during runtime once a language has been identifiedcorresponding to the input. The adapted analysis method for the finitestate machine engine can correspond more specifically to analysismethods for the identified language. Finally, the finite state machineengine may analyze future input data using the adapted analysis method.The feedback process may be iterative, so that additional identifyinginformation may be found in the input data to allow for furtheradaptation of the analysis method.

Programs (also referred to herein as “images”) for loading onto a finitestate machine engine can be generated by a compiler as discussed belowwith respect to FIG. 19 . In general, compiling can be a computationallyintensive process, and can be most apparent when compiling largedatabases of pattern signatures for the first time. In runtimeoperation, finite state machines engines of higher levels can beproviding feedback to the lower levels in the form of an incrementalprogram update for the lower level finite state machine engine. Thus,the feedback information to the lower level finite state machine enginecan be much smaller, incremental updates to an original program that areless computationally intensive to compile.

FIG. 14 illustrates another example of a four-level hierarchy 700implemented with four finite state machine engines 702, 704, 706, 708.Here, the second, third, and fourth level finite state machine engines704, 706, 708 receive input data from outputs of lower level as well asthe raw data stream. Accordingly, the levels two, three, and four canidentify patterns from combinations of the patterns from lower levelsand the raw data.

As can be seen from FIGS. 13 and 14 , finite state machine engines canbe cascaded in almost any manner where the raw data input to thehierarchy, as well as an output from a finite state machine engine, canbe sent to any other finite state machine engine, including itself.Moreover, the outputs from a given finite state machine engine can besent to another finite state machine engine as input data and/or asfeedback for updating the program for a finite state machine engine.

As noted above, due to the time for a finite state machine engine toprocess one bit (or word) of an input data stream, cascading finitestate machine engines in series can increase the time to fully processthe input data stream through all the finite state machine engines. Thelowest level of the hierarchy will often receive the lowest (mostgranular) level of input. Accordingly, the lower levels should beexpected to be more active than the output of high levels. That is, eachsuccessive level in the hierarchy can assemble higher level objects. Inan example, a finite state machine engine has a maximum input rate thatlimits how fast input data can be fed to the finite state machineengine. This input rate can be thought of as a single data cycle. Oneach successive data cycle, the finite state machine engine has thepotential to activate many final states. This could cause a finite statemachine engine (especially at the lowest level of the hierarchy) toproduce a significant amount of output (match) data. For example, if theinput is provided as a stream of bytes to the lowest level finite statemachine engine, on any given data cycle it may be possible for thefinite state machine engine to generate multiple bytes of stateinformation. If one byte of information can generate multiple bytes ofstate information, then the entire hierarchy of finite state machineengines should be synchronized so that information is passed up thehierarchy. The feedback does not need to be synchronized, however, thefaster the feedback is received at a lower lever, the faster the lowerlevel can adapt, and the more efficient the analysis.

As an example, a maximum size output for each level of the hierarchy(implemented with a single finite state machine engine) can equal 1024bytes and a depth of the hierarchy can equal 4 levels. The input datastream data rate for a finite state machine engine can equal 128MB/second. With these conditions each level of the hierarchy could betraversed in 7.63 microseconds. With a four level hierarchy, the totalsettling time of the entire stack of finite state machine engines wouldbe 4 times 7.63 microseconds or 30.5 microseconds. With a 30.5microsecond settling time, the implication is that the input datafrequency should be limited to 32 KB/s.

Notably, this is highly dependent on the configuration of the finitestate machine engines. Finite state machine engines can be configurableto tradeoff input data rates vs. the state machine size. In addition,the input word size to a finite state machine engine can be adjusted ifcorresponding modifications are made to the compiler that produced theindividual images loaded on the finite state machine engines.

In an example, the methods to implement one or more FSMs as describedwith reference to FIGS. 1-14 could be implemented with software on amachine having a von Nuemann architecture. Accordingly, softwareinstructions could cause a processor to implement a first level analysisFSM on a raw data stream. The output from the first level FSM could thenbe processed by the processor according to a second level FSM and so on.Furthermore, the feedback loop discussed above could be implemented by aprocessor that analyzes an output from a level of the FSM, and uses thatto generate a new FSM for one or more of the levels.

FIGS. 15-18 illustrate an example of a parallel machine referred toherein as “FSM engine 800”. In an example, the FSM engine 800 comprisesa hardware implementation of a finite state machine. Accordingly, theFSM engine 800 implements a plurality of selectively coupleable hardwareelements (e.g., programmable elements) that correspond to a plurality ofstates in a FSM. Similar to a state in a FSM, a hardware element cananalyze an input stream and activate a downstream hardware element basedon the input stream.

The FSM engine 800 includes a plurality of programmable elementsincluding general purpose elements and special purpose elements. Thegeneral purpose elements can be programmed to implement many differentfunctions. These general purpose elements include SMEs 804, 805 (shownin FIG. 18 ) that are hierarchically organized into rows 806 (shown inFIGS. 16 and 17 ) and blocks 802 (shown in FIGS. 15 and 16 ). To routesignals between the hierarchically organized SMEs 804, 805, a hierarchyof programmable switches is used including inter-block switches 803(shown in FIGS. 15 and 16 ), intra-block switches 808 (shown in FIGS. 9and 10 ) and intra-row switches 812 (shown in FIG. 17 ). A SME 804, 805can correspond to a state of a FSM implemented by the FSM engine 800.The SMEs 804, 805 can be coupled together by using the programmableswitches as described below. Accordingly, a FSM can be implemented onthe FSM engine 800 by programming the SMEs 804, 805 to correspond to thefunctions of states and by selectively coupling together the SMEs 804,805 to correspond to the transitions between states in the FSM.

FIG. 15 illustrates an overall view of an example FSM engine 800. TheFSM engine 800 includes a plurality of blocks 802 that can beselectively coupled together with programmable inter-block switches 803.Additionally, the blocks 802 can be selectively coupled to an inputblock 809 (e.g., a data input port) for receiving signals (e.g., data)and providing the data to the blocks 802. The blocks 802 can also beselectively coupled to an output block 813 (e.g., an output port) forproviding signals from the blocks 802 to an external device (e.g.,another FSM engine 800). The FSM engine 800 can also include aprogramming interface 811 to load a program (e.g., an image) onto theFSM engine 800. The image can program (e.g., set) the state of the SMEs804, 805. That is, the image can configure the SMEs 804, 805 to react ina certain way to a given input at the input block 809. For example, aSME 804 can be set to output a high signal when the character ‘a’ isreceived at the input block 809.

In an example, the input block 809, the output block 813, and/or theprogramming interface 811 can be implemented as registers such thatwriting to the registers provides data to or from the respectiveelements. Accordingly, bits from the image stored in the registerscorresponding to the programming interface 811 can be loaded on the SMEs804, 805. Although FIG. 15 illustrates a certain number of conductors(e.g., wire, trace) between a block 802, input block 809, output block813, and an inter-block switch 803, it should be understood that inother examples, fewer or more conductors can be used.

FIG. 16 illustrates an example of a block 802. A block 802 can include aplurality of rows 806 that can be selectively coupled together withprogrammable intra-block switches 808. Additionally, a row 806 can beselectively coupled to another row 806 within another block 802 with theinter-block switches 803. In an example, buffers 801 are included tocontrol the timing of signals to/from the inter-block switches 803. Arow 806 includes a plurality of SMEs 804, 805 organized into pairs ofelements that are referred to herein as groups of two (GOTs) 810. In anexample, a block 802 comprises sixteen (16) rows 806.

FIG. 17 illustrates an example of a row 806. A GOT 810 can beselectively coupled to other GOTs 810 and any other elements 824 withinthe row 806 by programmable intra-row switches 812. A GOT 810 can alsobe coupled to other GOTs 810 in other rows 806 with the intra-blockswitch 808, or other GOTs 810 in other blocks 802 with an inter-blockswitch 803. In an example, a GOT 810 has a first and second input 814,816, and an output 818. The first input 814 is coupled to a first SME804 of the GOT 810 and the second input 816 is coupled to a second SME805 of the GOT 810.

In an example, the row 806 includes a first and second plurality of rowinterconnection conductors 820, 822. In an example, an input 814, 816 ofa GOT 810 can be coupled to one or more row interconnection conductors820, 822, and an output 818 can be coupled to one row interconnectionconductor 820, 822. In an example, a first plurality of the rowinterconnection conductors 820 can be coupled to each SME 804 of eachGOT 810 within the row 806. A second plurality of the rowinterconnection conductors 822 can be coupled to one SME 804 of each GOT810 within the row 806, but cannot be coupled to the other SME 805 ofthe GOT 810. In an example, a first half of the second plurality of rowinterconnection conductors 822 can couple to first half of the SMEs 804within a row 806 (one SME 804 from each GOT 810) and a second half ofthe second plurality of row interconnection conductors 822 can couple toa second half of the SMEs 805 within a row 806 (the other SME 804 fromeach GOT 810). The limited connectivity between the second plurality ofrow interconnection conductors 822 and the SMEs 804, 805 is referred toherein as “parity”. In an example, the row 806 can also include aspecial purpose element 824 such as a counter, a programmable Booleanlogic element, a field programmable gate array (FPGA), an applicationspecific integrated circuit (ASIC), a programmable processor (e.g., amicroprocessor), and other elements.

In an example, the special purpose element 824 includes a counter (alsoreferred to herein as counter 824). In an example, the counter 824comprises a 12-bit programmable down counter. The 12-bit programmablecounter 824 has a counting input, a reset input, and zero-count output.The counting input, when asserted, decrements the value of the counter824 by one. The reset input, when asserted, causes the counter 824 toload an initial value from an associated register. For the 12-bitcounter 824, up to a 12-bit number can be loaded in as the initialvalue. When the value of the counter 824 is decremented to zero (0), thezero-count output is asserted. The counter 824 also has at least twomodes, pulse and hold. When the counter 824 is set to pulse mode, thezero-count output is asserted during the clock cycle when the counter824 decrements to zero, and at the next clock cycle the zero-countoutput is no longer asserted. When the counter 824 is set to hold modethe zero-count output is asserted during the clock cycle when thecounter 824 decrements to zero, and stays asserted until the counter 824is reset by the reset input being asserted. In an example, the specialpurpose element 824 includes Boolean logic. In some examples, thisBoolean logic can be used to extract information from terminal stateSMEs in FSM engine 800. The information extracted can be used totransfer state information to other FSM engines 800 and/or to transferprogramming information used to reprogram FSM engine 800, or toreprogram another FSM engine 800.

FIG. 18 illustrates an example of a GOT 810. The GOT 810 includes afirst SME 804 and a second SME 805 having inputs 814, 816 and havingtheir outputs 826, 828 coupled to an OR gate 830 and a 3-to-1multiplexer 842. The 3-to-1 multiplexer 842 can be set to couple theoutput 818 of the GOT 810 to either the first SME 804, the second SME805, or the OR gate 830. The OR gate 830 can be used to couple togetherboth outputs 826, 828 to form the common output 818 of the GOT 810. Inan example, the first and second SME 804, 805 exhibit parity, asdiscussed above, where the input 814 of the first SME 804 can be coupledto some of the row interconnect conductors 822 and the input 816 of thesecond SME 805 can be coupled to other row interconnect conductors 822.In an example, the two SMEs 804, 805 within a GOT 810 can be cascadedand/or looped back to themselves by setting either or both of switches840. The SMEs 804, 805 can be cascaded by coupling the output 826, 828of the SMEs 804, 805 to the input 814, 816 of the other SME 804, 805.The SMEs 804, 805 can be looped back to themselves by coupling theoutput 826, 828 to their own input 814, 816. Accordingly, the output 826of the first SME 804 can be coupled to neither, one, or both of theinput 814 of the first SME 804 and the input 816 of the second SME 805.

In an example, a state machine element 804, 805 comprises a plurality ofmemory cells 832, such as those often used in dynamic random accessmemory (DRAM), coupled in parallel to a detect line 834. One such memorycell 832 comprises a memory cell that can be set to a data state, suchas one that corresponds to either a high or a low value (e.g., a 1 or0). The output of the memory cell 832 is coupled to the detect line 834and the input to the memory cell 832 receives signals based on data onthe data stream line 836. In an example, an input on the data streamline 836 is decoded to select one of the memory cells 832. The selectedmemory cell 832 provides its stored data state as an output onto thedetect line 834. For example, the data received at the data input port809 can be provided to a decoder (not shown) and the decoder can selectone of the data stream lines 836. In an example, the decoder can convertan ACSII character to 1 of 256 bits.

A memory cell 832, therefore, outputs a high signal to the detect line834 when the memory cell 832 is set to a high value and the data on thedata stream line 836 corresponds to the memory cell 832. When the dataon the data stream line 836 corresponds to the memory cell 832 and thememory cell 832 is set to a low value, the memory cell 832 outputs a lowsignal to the detect line 834. The outputs from the memory cells 832 onthe detect line 834 are sensed by a detect circuit 838. In an example,the signal on an input line 814, 816 sets the respective detect circuit838 to either an active or inactive state. When set to the inactivestate, the detect circuit 838 outputs a low signal on the respectiveoutput 826, 828 regardless of the signal on the respective detect line834. When set to an active state, the detect circuit 838 outputs a highsignal on the respective output line 826, 828 when a high signal isdetected from one of the memory cells 834 of the respective SME 804,805. When in the active state, the detect circuit 838 outputs a lowsignal on the respective output line 826, 828 when the signals from allof the memory cells 834 of the respective SME 804, 805 are low.

In an example, an SME 804, 805 includes 256 memory cells 832 and eachmemory cell 832 is coupled to a different data stream line 836. Thus, anSME 804, 805 can be programmed to output a high signal when a selectedone or more of the data stream lines 836 have a high signal thereon. Forexample, the SME 804 can have a first memory cell 832 (e.g., bit 0) sethigh and all other memory cells 832 (e.g., bits 1-255) set low. When therespective detect circuit 838 is in the active state, the SME 804outputs a high signal on the output 826 when the data stream line 836corresponding to bit 0 has a high signal thereon. In other examples, theSME 804 can be set to output a high signal when one of multiple datastream lines 836 have a high signal thereon by setting the appropriatememory cells 832 to a high value.

In an example, a memory cell 832 can be set to a high or low value byreading bits from an associated register. Accordingly, the SMEs 804 canbe programmed by storing an image created by the compiler into theregisters and loading the bits in the registers into associated memorycells 832. In an example, the image created by the compiler includes abinary image of high and low (e.g., 1 and 0) bits. The image can programthe FSM engine 800 to operate as a FSM by cascading the SMEs 804, 805.For example, a first SME 804 can be set to an active state by settingthe detect circuit 838 to the active state. The first SME 804 can be setto output a high signal when the data stream line 836 corresponding tobit 0 has a high signal thereon. The second SME 805 can be initially setto an inactive state, but can be set to, when active, output a highsignal when the data stream line 836 corresponding to bit 1 has a highsignal thereon. The first SME 804 and the second SME 805 can be cascadedby setting the output 826 of the first SME 804 to couple to the input816 of the second SME 805. Thus, when a high signal is sensed on thedata stream line 836 corresponding to bit 0, the first SME 804 outputs ahigh signal on the output 826 and sets the detect circuit 838 of thesecond SME 805 to an active state. When a high signal is sensed on thedata stream line 836 corresponding to bit 1, the second SME 805 outputsa high signal on the output 828 to activate another SME 504, SME 805 orfor output from the FSM engine 800.

FIG. 19 illustrates an example of a method 1000 for a compiler toconvert source code into an image configured to program a parallelmachine. Method 1000 includes parsing the source code into a syntax tree(block 1002), converting the syntax tree into an automaton (block 1004),optimizing the automaton (block 1006), converting the automaton into anetlist (block 1008), placing the netlist on hardware (block 1010),routing the netlist (block 1012), and publishing the resulting image(block 1014).

In an example, the compiler includes an application programminginterface (API) that allows software developers to create images forimplementing FSMs on the FSM engine 800. The compiler provides methodsto convert an input set of regular expressions in the source code intoan image that is configured to program the FSM engine 800. The compilercan be implemented by instructions for a computer having a von Neumannarchitecture. These instructions can cause a processor on the computerto implement the functions of the compiler. For example, theinstructions, when executed by the processor, can cause the processor toperform actions as described in blocks 1002, 1004, 1006, 1008, 1010,1012, and 1014 on source code that is accessible to the processor. Anexample computer having a von Neumann architecture is shown in FIG. 20and described below.

In an example, the source code describes search strings for identifyingpatterns of symbols within a group of symbols. To describe the searchstrings, the source code can include a plurality of regular expressions(regexs). A regex can be a string for describing a symbol searchpattern. Regexes are widely used in various computer domains, such asprogramming languages, text editors, network security, and others. In anexample, the regular expressions supported by the compiler includesearch criteria for the search of unstructured data. Unstructured datacan include data that is free form and has no indexing applied to wordswithin the data. Words can include any combination of bytes, printableand non-printable, within the data. In an example, the compiler cansupport multiple different source code languages for implementingregexes including Perl, (e.g., Perl compatible regular expressions(PCRE)), PUP, Java, and NET languages.

At block 1002 the compiler can parse the source code to form anarrangement of relationally connected operators, where different typesof operators correspond to different functions implemented by the sourcecode (e.g., different functions implemented by regexes in the sourcecode). Parsing source code can create a generic representation of thesource code. In an example, the generic representation comprises anencoded representation of the regexs in the source code in the form of atree graph known as a syntax tree. The examples described herein referto the arrangement as a syntax tree (also known as an “abstract syntaxtree”) in other examples, however, a concrete syntax tree or otherarrangement can be used.

Since, as mentioned above, the compiler can support multiple languagesof source code, parsing converts the source code, regardless of thelanguage, into a non-language specific representation, e.g., a syntaxtree. Thus, further processing (blocks 1004, 1006, 1008, 1010) by thecompiler can work from a common input structure regardless of thelanguage of the source code.

As noted above, the syntax tree includes a plurality of operators thatare relationally connected. A syntax tree can include multiple differenttypes of operators. That is, different operators can correspond todifferent functions implemented by the regexes in the source code.

At block 1004, the syntax tree is converted into an automaton. Anautomaton comprises a software model of a FSM and can accordingly beclassified as deterministic or non-deterministic. A deterministicautomaton has a single path of execution at a given time, while anon-deterministic automaton has multiple concurrent paths of execution.The automaton comprises a plurality of states. In order to convert thesyntax tree into an automaton, the operators and relationships betweenthe operators in the syntax tree are converted into states withtransitions between the states. In an example, the automaton can beconverted based partly on the hardware of the FSM engine 800.

In an example, input symbols for the automaton include the symbols ofthe alphabet, the numerals 0-9, and other printable characters. In anexample, the input symbols are represented by the byte values 0 through255 inclusive. In an example, an automaton can be represented as adirected graph where the nodes of the graph correspond to the set ofstates. In an example, a transition from state p to state q on an inputsymbol α, i.e. δ(p,α), is shown by a directed connection from node p tonode q. In an example, a reversal of an automaton produces a newautomaton where each transition p→q on some symbol α is reversed q→p onthe same symbol. In a reversal, start state becomes a final state andthe final states become start states. In an example, the languageaccepted (e.g., matched) by an automaton is the set of all possiblecharacter strings which when input sequentially into the automaton willreach a final state. Each string in the language accepted by theautomaton traces a path from the start state to one or more finalstates.

At block 1006, after the automaton is constructed, the automaton isoptimized to, among other things, reduce its complexity and size. Theautomaton can be optimized by combining redundant states.

At block 1008, the optimized automaton is converted into a netlist.Converting the automaton into a netlist maps each state of the automatonto a hardware element (e.g., SMEs 804, 805, other elements 824) on theFSM engine 800, and determines the connections between the hardwareelements.

At block 1010, the netlist is placed to select a specific hardwareelement of the target device (e.g., SMEs 804, 805, special purposeelements 824) corresponding to each node of the netlist. In an example,placing selects each specific hardware element based on general inputand output constraints for of the FSM engine 800.

At block 1012, the placed netlist is routed to determine the settingsfor the programmable switches (e.g., inter-block switches 803,intra-block switches 808, and intra-row switches 812) in order to couplethe selected hardware elements together to achieve the connectionsdescribe by the netlist. In an example, the settings for theprogrammable switches are determined by determining specific conductorsof the FSM engine 800 that will be used to connect the selected hardwareelements, and the settings for the programmable switches. Routing cantake into account more specific limitations of the connections betweenthe hardware elements than placement at block 1010. Accordingly, routingmay adjust the location of some of the hardware elements as determinedby the global placement in order to make appropriate connections giventhe actual limitations of the conductors on the FSM engine 800.

Once the netlist is placed and routed, the placed and routed netlist canbe converted into a plurality of bits for programming of a FSM engine800. The plurality of bits are referred to herein as an image.

At block 1014, an image is published by the compiler. The imagecomprises a plurality of bits for programming specific hardware elementsand/or programmable switches of the FSM engine 800. In embodiments wherethe image comprises a plurality of bits (e.g., 0 and 1), the image canbe referred to as a binary image. The bits can be loaded onto the FSMengine 800 to program the state of SMEs 804, 805, the special purposeelements 824, and the programmable switches such that the programmed FSMengine 800 implements a FSM having the functionality described by thesource code. Placement (block 1010) and routing (block 1012) can mapspecific hardware elements at specific locations in the FSM engine 800to specific states in the automaton. Accordingly, the bits in the imagecan program the specific hardware elements and/or programmable switchesto implement the desired function(s). In an example, the image can bepublished by saving the machine code to a computer readable medium. Inanother example, the image can be published by displaying the image on adisplay device. In still another example, the image can be published bysending the image to another device, such as a programming device forloading the image onto the FSM engine 800. In yet another example, theimage can be published by loading the image onto a parallel machine(e.g., the FSM engine 800).

In an example, an image can be loaded onto the FSM engine 800 by eitherdirectly loading the bit values from the image to the SMEs 804, 805 andother hardware elements 824 or by loading the image into one or moreregisters and then writing the bit values from the registers to the SMEs804, 805 and other hardware elements 824. In an example, the state ofthe programmable switches (e.g., inter-block switches 803, intra-blockswitches 808, and intra-row switches 812). In an example, the hardwareelements (e.g., SMEs 804, 805, other elements 824, programmable switches803, 808, 812) of the FSM engine 800 are memory mapped such that aprogramming device and/or computer can load the image onto the FSMengine 800 by writing the image to one or more memory addresses.

Method examples described herein can be machine or computer-implementedat least in part. Some examples can include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples. An implementation of such methods can include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code can include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. Further, the code may be tangibly stored on one ormore volatile or non-volatile computer-readable media during executionor at other times. These computer-readable media may include, but arenot limited to, hard disks, removable magnetic disks, removable opticaldisks (e.g., compact disks and digital video disks), magnetic cassettes,memory cards or sticks, random access memories (RAMs), read onlymemories (ROMs), and the like.

FIG. 20 illustrates generally an example of a computer 1500 having a vonNeumann architecture. Upon reading and comprehending the content of thisdisclosure, one of ordinary skill in the art will understand the mannerin which a software program can be launched from a computer-readablemedium in a computer-based system to execute the functions defined inthe software program. One of ordinary skill in the art will furtherunderstand the various programming languages that can be employed tocreate one or more software programs designed to implement and performthe methods disclosed herein. The programs can be structured in anobject-orientated format using an object-oriented language, such asJava, C++, or one or more other languages. Alternatively, the programscan be structured in a procedure-orientated format using a procedurallanguage, such as assembly, C, etc. The software components cancommunicate using any of a number of mechanisms well known to those ofordinary skill in the art, such as application program interfaces orinterprocess communication techniques, including remote procedure callsor others. The teachings of various embodiments are not limited to anyparticular programming language or environment.

Thus, other embodiments can be realized. For example, an article ofmanufacture, such as a computer, a memory system, a magnetic or opticaldisk, some other storage device, or any type of electronic device orsystem can include one or more processors 1502 coupled to acomputer-readable medium 1522 such as a memory (e.g., removable storagemedia, as well as any memory including an electrical, optical, orelectromagnetic conductor) having instructions 1524 stored thereon(e.g., computer program instructions), which when executed by the one ormore processors 1502 result in performing any of the actions describedwith respect to the methods above.

The computer 1500 can take the form of a computer system having aprocessor 1502 coupled to a number of components directly, and/or usinga bus 1508. Such components can include main memory 1504, static ornon-volatile memory 1506, and mass storage 1516. Other componentscoupled to the processor 1502 can include an output device 1510, such asa video display, an input device 1512, such as a keyboard, and a cursorcontrol device 1514, such as a mouse. A network interface device 1520 tocouple the processor 1502 and other components to a network 1526 canalso be coupled to the bus 1508. The instructions 1524 can further betransmitted or received over the network 1526 via the network interfacedevice 1520 utilizing any one of a number of well-known transferprotocols (e.g., HTTP). Any of these elements coupled to the bus 1508can be absent, present singly, or present in plural numbers, dependingon the specific embodiment to be realized.

In an example, one or more of the processor 1502, the memories 1504,1506, or the storage device 1516 can each include instructions 1524that, when executed, can cause the computer 1500 to perform any one ormore of the methods described herein. In alternative embodiments, thecomputer 1500 operates as a standalone device or can be connected (e.g.,networked) to other devices. In a networked environment, the computer1500 can operate in the capacity of a server or a client device inserver-client network environment, or as a peer device in a peer-to-peer(or distributed) network environment. The computer 1500 can include apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a cellular telephone, a web appliance, anetwork router, switch or bridge, or any device capable of executing aset of instructions (sequential or otherwise) that specify actions to betaken by that device. Further, while only a single computer 1500 isillustrated, the term “computer” shall also be taken to include anycollection of devices that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The computer 1500 can also include an output controller 1528 forcommunicating with peripheral devices using one or more communicationprotocols (e.g., universal serial bus (USB), IEEE 1394, etc.) The outputcontroller 1528 can, for example, provide an image to a programmingdevice 1530 that is communicatively coupled to the computer 1500. Theprogramming device 1530 can be configured to program a parallel machine(e.g., parallel machine 100, FSM engine 800). In other examples, theprogramming device 1530 can be integrated with the computer 1500 andcoupled to the bus 1508 or can communicate with the computer 1500 viathe network interface device 1520 or another device.

While the computer-readable medium 1524 is shown as a single medium, theterm “computer-readable medium” should be taken to include a singlemedium or multiple media (e.g., a centralized or distributed database,or associated caches and servers, and or a variety of storage media,such as the processor 1502 registers, memories 1504, 1506, and thestorage device 1516) that store the one or more sets of instructions1524. The term “computer-readable medium” shall also be taken to includeany medium that is capable of storing, encoding or carrying a set ofinstructions for execution by the computer and that cause the computerto perform any one or more of the methodologies of the presentinvention, or that is capable of storing, encoding or carrying datastructures utilized by or associated with such a set of instructions.The term “computer-readable medium” shall accordingly be taken toinclude, but not be limited to tangible media, such as solid-statememories, optical, and magnetic media.

The Abstract is provided to comply with 37 C.F.R. Section 1.72(b)requiring an abstract that will allow the reader to ascertain the natureand gist of the technical disclosure. It is submitted with theunderstanding that it will not be used to limit or interpret the scopeor meaning of the claims. The following claims are hereby incorporatedinto the detailed description, with each claim standing on its own as aseparate embodiment.

Example Embodiments

Example 1 includes a programmable device having a plurality ofprogrammable elements, wherein the programmable elements are configuredto implement one or more finite state machines, wherein the plurality ofprogrammable elements are configured to receive a N-digit input andprovide a M-digit output as a function of the N-digit input, wherein theM-digit output includes state information from less than all of theprogrammable elements.

Example 2 includes a hierarchical parallel machine having a firstparallel machine comprising a plurality of programmable elements,wherein the programmable elements are configured to implement one ormore finite state machines, wherein the plurality of programmableelements are configured to receive a N-digit input and provide a M-digitoutput as a function of the N-digit input, wherein the M-digit outputincludes state information from less than all of the programmableelements; and a second parallel machine configured to receive andprocess at least part of the M-digit output.

Example 3 includes a programmable device having a plurality ofprogrammable elements wherein the programmable elements are configuredto implement one or more finite state machines, wherein the plurality ofprogrammable elements are configured to receive a N-digit input andprovide a M-digit output as a function of the N-digit input, wherein theM-digit output is formed by compressing state information from each ofthe programmable elements.

Example 4 includes a hierarchical parallel machine having a firstparallel machine comprising a plurality of programmable elements,wherein the programmable elements are configured to implement one ormore finite state machines, wherein the plurality of programmableelements are configured to receive a N-digit input and provide a M-digitoutput as a function of the N-digit input, wherein the M-digit output isformed by compressing state information from each of the programmableelements.

Example 5 includes a method of providing state information from aparallel machine to another device, wherein the parallel machineincludes a plurality of programmable elements, wherein each of theprogrammable elements is configured to have a corresponding state. Themethod includes determining state information, wherein the stateinformation comprises the state of each of the programmable elements inthe parallel machine; compressing the state information; and providingthe compressed state information to the other device

Example 6 includes a hierarchical parallel machine having a first levelparallel machine having at least one N-digit input and a plurality ofN-digit outputs, wherein each of the N-digit outputs corresponds to arespective group of N state machines implemented on the first levelparallel machine.

Example 7 includes a parallel machine comprising a plurality ofprogrammable elements configured to implement at least one finite statemachine. The parallel making is configured to determine stateinformation, wherein the state information comprises the state of eachof the programmable elements; compress the state information; andprovide the compressed state information to another device.

In Example 8, the subject matter of any of Examples 1-7 can optionallyinclude wherein the plurality of programmable elements comprises one oftwo or more groups of programmable elements.

In Example 9, the subject matter of any of Examples 1-8 can optionallyinclude an N-digit input interface coupled to the one group ofprogrammable elements and configured to receive the N-digit input; and aM-digit output interface coupled to the one group of programmableelements and configured to provide the M-digit output

In Example 10, the subject matter of any of Examples 1-9 can optionallyinclude wherein the one group of programmable elements comprises a blockof programmable elements.

In Example 11, the subject matter of any of Examples 1-10 can optionallyinclude wherein the block of programmable elements comprises a pluralityof rows of programmable elements, wherein each of the rows is coupled toa respective one of a plurality of intra-block switches.

In Example 12, the subject matter of any of Examples 1-11 can optionallyinclude wherein the programmable elements in each of the rows comprises:a plurality of groups of two state machine elements; and anotherprogrammable element.

In Example 13, the subject matter of any of Examples 1-12 can optionallyinclude a programmable switch configured to selectively couple the onegroup of programmable elements to another one of the groups ofprogrammable elements, an input port, and/or an output port.

In Example 14, the subject matter of any of Examples 1-13 can optionallyinclude a register configured to store a program configured to programthe plurality of programmable elements and the plurality of programmableswitches.

In Example 15, the subject matter of any of Examples 1-14 can optionallyinclude wherein N equals M.

In Example 16, the subject matter of any of Examples 1-15 can optionallyinclude wherein M is an integer multiple of N.

In Example 17, the subject matter of any of Examples 1-16 can optionallyinclude OR logic configured to aggregate output from two or more of theprogrammable elements used to implement a same one of the finite statemachines.

In Example 18, the subject matter of any of Examples 1-17 can optionallyinclude wherein the programmable elements are configured to implement alogical group of N state machines, wherein the outputs of the N statemachines are aggregated to provide the M-digit output.

In Example 19, the subject matter of any of Examples 1-18 can optionallyinclude wherein the logic comprises an OR gate.

In Example 20, the subject matter of any of Examples 1-19 can optionallyinclude wherein the state information included in the M-digit outputcomprises compressed state information.

In Example 21, the subject matter of any of Examples 1-20 can optionallyinclude wherein the plurality of programmable elements comprise statemachine elements.

In Example 22, the subject matter of any of Examples 1-21 can optionallyinclude wherein the M-digit output comprises a difference vector.

In Example 23, the subject matter of any of Examples 1-22 can optionallyinclude wherein each state in the implemented one or more finite statemachines corresponds to a respective digit in a state vector, andwherein the difference vector includes only those digits in the statevector that change in response to an input symbol provided to theprogrammable device.

In Example 24, the subject matter of any of Examples 1-23 can optionallyinclude wherein each state in the implemented one or more finite statemachines corresponds to a respective digit in a state vector, andwherein the M-digit output comprises only a subset of the digits in thestate vector.

In Example 25, the subject matter of any of Examples 1-24 can optionallyinclude wherein the subset of the digits comprises those digitscorresponding to final states in the one or more finite state machines.

In Example 26, the subject matter of any of Examples 1-25 can optionallyinclude wherein all state machines implemented in the device receive theN-digit input.

In Example 27, the subject matter of any of Examples 1-26 can optionallyinclude wherein the plurality of programmable elements comprise one oftwo or more groups of programmable elements, wherein each of the groupshas its own dedicated input.

In Example 28, the subject matter of any of Examples 1-27 can optionallyinclude wherein the N-digit input is on a bottom of a semiconductor die,and wherein the M-digit output is on a top of the semiconductor die.

In Example 29, the subject matter of any of Examples 1-28 can optionallyinclude wherein the plurality of programmable elements comprise one oftwo or more groups of programmable elements, and wherein theprogrammable device is configured to provide state information from oneof the groups to another one of the groups in the programmable device.

In Example 30, the subject matter of any of Examples 1-29 can optionallyinclude wherein the second parallel machine is configured to receive andprocess the whole M-digit output.

In Example 31, the subject matter of any of Examples 1-30 can optionallyinclude an input bus coupled to the first parallel machine andconfigured to provide the N-digit input; and an output bus coupledbetween the first parallel machine and the second parallel machine, theoutput bus being configured to provide at least part of the M-digitoutput to the second parallel machine.

In Example 32, the subject matter of any of Examples 1-31 can optionallyinclude wherein the input bus and output bus are equal in size.

In Example 33, the subject matter of any of Examples 1-32 can optionallyinclude wherein the corresponding groups in the first and secondparallel machines are coupled by a respective group of interconnects.

In Example 34, the subject matter of any of Examples 1-33 can optionallyinclude wherein the M-digit output is provided to each state machineimplemented in the second parallel machine.

In Example 35, the subject matter of any of Examples 1-34 can optionallyinclude wherein the second parallel machine comprises a plurality ofprogrammable elements grouped into a plurality of groups, wherein theM-digit output is provided to a respective one of the groups accordingto a pre-defined manner.

In Example 36, the subject matter of any of Examples 1-35 can optionallyinclude wherein the second parallel machine comprises a plurality ofprogrammable elements configured to send address information to thesecond parallel machine, wherein the address information indicates towhich of the groups in the second parallel machine the M-digit output isbeing provided.

In Example 37, the subject matter of any of Examples 1-36 can optionallyinclude wherein the parallel machines are stacked.

In Example 38, the subject matter of any of Examples 1-37 can optionallyinclude wherein the plurality of programmable elements comprise one oftwo or more groups of programmable elements, further comprising logiccorresponding to each group, wherein the logic corresponding to arespective one of the groups aggregates state information from two ormore programmable elements in that group, and wherein one or more digitsof the M-digit output from that group is a function of such logic.

In Example 39, the subject matter of any of Examples 1-38 can optionallyinclude wherein the M-digit output is formed by compressing stateinformation from the programmable elements.

In Example 40, the subject matter of any of Examples 1-39 can optionallyinclude logic configured to aggregate output from two or more of theprogrammable elements used to implement a same one of the finite statemachines.

In Example 41, the subject matter of any of Examples 1-40 can optionallyinclude wherein the programmable elements are configured to implement alogical group of N state machines, wherein the outputs of the N statemachines are aggregated to provide the M-digit output.

In Example 42, the subject matter of any of Examples 1-41 can optionallyinclude wherein compressing the state information includes applying alossless compression algorithm to the state information.

In Example 43, the subject matter of any of Examples 1-42 can optionallyinclude wherein providing the compressed state information to the otherdevice comprises providing the compressed state information to anotherparallel machine.

In Example 44, the subject matter of any of Examples 1-43 can optionallyinclude wherein providing the compressed state information to the otherdevice comprises providing the compressed state information to systemmemory.

In Example 45, the subject matter of any of Examples 1-44 can optionallyinclude wherein compressing the state information comprises aggregatingfinal states in a finite state machine implemented on the parallelmachine.

In Example 46, the subject matter of any of Examples 1-45 can optionallyinclude a first level parallel machine having at least one N-digit inputand a plurality of N-digit outputs, wherein each of the N-digit outputscorresponds to a respective group of N state machines implemented on thefirst level parallel machine.

In Example 47, the subject matter of any of Examples 1-46 can optionallyinclude wherein at least one of the state machines implemented on thefirst level parallel machine includes a plurality of programmableelements corresponding to a plurality of final states of the at leastone state machine, wherein the output of the plurality of programmableelements corresponding to the plurality of final states are aggregatedtogether to provide one digit of one of the N-digit outputs.

In Example 48, the subject matter of any of Examples 1-47 can optionallyinclude wherein data provided on one of the N-digit outputs encodes thestatus of the final states for the respective group of N state machinesimplemented on the first level parallel machine.

In Example 49, the subject matter of any of Examples 1-48 can optionallyinclude wherein the first level parallel machine comprises a finitestate machine engine.

In Example 50, the subject matter of any of Examples 1-49 can optionallyinclude wherein the finite state machine engine comprises an array ofgroups of programmable elements, and wherein each of the groups ofprogrammable elements is coupled to a respective one of the N-digitoutputs.

In Example 51, the subject matter of any of Examples 1-50 can optionallyinclude wherein the first level parallel machine has a plurality ofN-digit input and wherein each of the groups of programmable elements iscoupled to a respective one of the N-digit inputs of the first levelparallel machine.

In Example 52, the subject matter of any of Examples 1-51 can optionallyinclude wherein the second level parallel machine comprises a finitestate machine engine.

In Example 53, the subject matter of any of Examples 1-52 can optionallyinclude wherein the finite state machine engine comprises an array ofgroups of programmable elements, and wherein each of the groups ofprogrammable elements is coupled to a respective one of the N-digitinputs.

In Example 54, the subject matter of any of Examples 1-53 can optionallyinclude wherein the second level parallel machine has a plurality ofN-digit outputs and wherein each of the groups of programmable elementsis coupled to a respective one of the N-digit output of the second levelparallel machine.

In Example 55, the subject matter of any of Examples 1-54 can optionallyinclude wherein the first parallel machine comprises a first die, andthe second parallel machine comprises a second die stacked with thefirst die.

In Example 56, subject matter of any of Examples 1-55 can optionallyinclude further comprising a third parallel machine and a bus, whereinthe third parallel machine comprises a third dies stacked with the firstdie and the second die, wherein the second die is between the first dieand the third die in the stack, and wherein the bus is configured totransfer state information between the first parallel machine and thethird parallel machine.

In Example 57, the subject matter of any of Examples 1-56 can optionallyinclude wherein the bus comprises a plurality of interconnects.

In Example 58, the subject matter of any of Examples 1-57 can optionallyinclude wherein the interconnects comprise through via interconnects.

In Example 59, the subject matter of any of Examples 1-58 can optionallyinclude wherein the parallel machines comprise finite state machineengines.

In Example 60, the subject matter of any of Examples 1-59 can optionallyinclude wherein the finite state machine engines comprise patternrecognition processors.

In Example 61, the subject matter of any of Examples 1-60 can optionallyinclude wherein the parallel machines comprise field programmable gatearrays.

In Example 62, the subject matter of any of Examples 1-61 can optionallyinclude wherein the at least one N-digit input of the first levelparallel machine is configured to receive raw data.

In Example 63, the subject matter of any of Examples 1-62 can optionallyinclude wherein each of the N-digit inputs of the second level parallelmachine correspond to a respective group of N state machines implementedon the second level parallel machine, wherein each group of N statemachines implemented on the second level parallel machine is driven byup to N state machines implemented on the first level parallel machine.

In Example 64, the subject matter of any of Examples 1-63 can optionallyinclude wherein the other device comprises a second parallel machine,wherein the second parallel machine is configured to receive and processthe compressed state information.

In Example 65, the subject matter of any of Examples 1-64 can optionallyinclude wherein the parallel machine being configured to compress thestate information comprises the parallel machine being configured toaggregate final states of a finite state machine implemented on theparallel machine.

In Example 66, the subject matter of any of Examples 1-65 can optionallyinclude Boolean logic configured to aggregate the final states.

In Example 67, the subject matter of any of Examples 1-66 can optionallyinclude wherein the parallel machine being configured to compress thestate information comprises the parallel machine being configured tooutput a difference vector, wherein the difference vector identifiesonly those states that have changed in response to an input symbol.

In Example 68, the subject matter of any of Examples 1-67 can optionallyinclude wherein the parallel machine being configured to compress thestate information comprises the parallel machine being configured tooutput an output vector, wherein the output vector only provides stateinformation for final states in a finite state machine implemented onthe parallel machine.

1. A device, comprising: a first processor, wherein the first processoris configured to utilize finite state machine circuitry of the device tosearch an input data stream to detect a pattern and generate a resultindicative of detection of the pattern; and a second processor coupledto the first processor, wherein the first processor is configured totransmit the result to the second processor.
 2. The device of claim 1,wherein the finite state machine circuitry comprises a plurality ofprogrammable elements.
 3. The device of claim 2, wherein the finitestate machine circuitry is configured to receive a programminginformation based upon a compiled expression to program the plurality ofprogrammable elements.
 4. The device of claim 3, wherein the compiledexpression comprises a regular expression (RegEx).
 5. The device ofclaim 1, wherein the second processor is configured to perform a secondsearch utilizing the result.
 6. A system, comprising: a data processingengine, wherein the data processing engine comprises: a first inputconfigured to receive a data stream; a second input configured toreceive programming instructions; finite state machine circuitryconfigured to search the data stream based upon the programminginstructions to detect a pattern; and an output configured to transmit aresult indicative of detection of the pattern.
 7. The system of claim 6,comprising a second data processing engine, wherein the second dataprocessing engine comprises: a third input configured to receive theresult from the data processing engine.
 8. The system of claim 7,wherein the second data processing engine comprises second finite statemachine circuitry configured to perform a second search utilizing theresult.
 9. The system of claim 8, wherein the second finite statemachine circuitry comprises a plurality of programmable elements. 10.The system of claim 9, wherein the second finite state machine circuitrycomprises a fourth input configured to receive second programminginstructions.
 11. The system of claim 10, wherein the second dataprocessing engine is configured to program the plurality of programmableelements utilizing the second programming instructions.
 12. The systemof claim 6, wherein the finite state machine circuitry comprises aplurality of programmable elements.
 13. The system of claim 12, whereinthe data processing engine is configured to program the plurality ofprogrammable elements utilizing the programming instructions.
 14. Thesystem of claim 13, comprising a compiler configured to generate theprogramming instructions based upon an expression.
 15. The system ofclaim 14, wherein the expression comprises a regular expression (RegEx).16. A method, comprising: receiving a data stream at a data processingengine; receiving programming instructions at the data processingengine; programming finite state machine circuitry of the a dataprocessing engine utilizing the programming instructions to search thedata stream to detect a pattern; and transmitting a result indicative ofdetection of the pattern from the data processing engine.
 17. The methodof claim 16, comprising receiving the result from the data processingengine at a second data processing engine.
 18. The method of claim 17,comprising performing a second search utilizing the result at the seconddata processing engine.
 19. The method of claim 18, comprising utilizingsecond finite state machine circuitry of the second data processingengine to perform the second search.
 20. The method of claim 19,comprising programming programmable elements of the second finite statemachine circuitry prior to performing the second search.